Key result
Proper application and reporting of traditional and emerging statistical methods is required to improve scientific rigor in biomarker studies in critical care and perioperative medicine.
Why the study?
Biomarker studies in critical care and perioperative medicine frequently contain statistical flaws, requiring proper application of traditional and emerging statistical methods to improve scientific rigor.
Answers are grounded in this paper and cite their sources.
This article provides a guide to traditional and emerging statistical methods for evaluating biomarkers in critical care and perioperative medicine to improve scientific rigor.
Interest in developing and using novel biomarkers in critical care and perioperative medicine is increasing. Biomarkers studies are often presented with flaws in the statistical analysis that preclude them from providing a scientifically valid and clinically relevant message for clinicians. To improve scientific rigor, the proper application and reporting of traditional and emerging statistical methods (e.g., machine learning) of biomarker studies is required. This Readers' Toolbox article aims to be a starting point to nonexpert readers and investigators to understand traditional and emerging research methods to assess biomarkers in critical care and perioperative medicine.
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Soussi et al. (2020) conducted a review in Critical care and perioperative medicine. Proper application and reporting of traditional and emerging statistical methods is required to improve scientific rigor in biomarker studies in critical care and perioperative medicine.
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